Assignment 2: Spatial Analysis and Visualization

Healthcare Access and Equity in Pennsylvania

Author

Jinheng

Published

October 7, 2025

Assignment Overview

Learning Objectives:

Apply spatial operations to answer policy-relevant research questions
Integrate census demographic data with spatial analysis
Create publication-quality visualizations and maps
Work with spatial data from multiple sources
Communicate findings effectively for policy audiences

Part 1: Healthcare Access for Vulnerable Populations

Research Question

Which Pennsylvania counties have the highest proportion of vulnerable populations (elderly + low-income) living far from hospitals?

Your analysis should identify counties that should be priorities for healthcare investment and policy intervention.

Required Analysis Steps

Complete the following analysis, documenting each step with code and brief explanations:

Step 1: Data Collection (5 points)

Load the required spatial data:

Pennsylvania county boundaries
Pennsylvania hospitals (from lecture data)
Pennsylvania census tracts

Your Task:

# Load required packages
library(sf)
library(tidyverse)
library(tigris)
library(tidycensus)
library(scales)
library(patchwork)
library(here)
library(knitr)

# Add this near the top of your .qmd after loading libraries
options(tigris_use_cache = TRUE)
options(tigris_progress = FALSE)  # Suppress tigris progress bars

# Load spatial data
pa_counties <- st_read("data/Pennsylvania_County_Boundaries.shp")

Reading layer `Pennsylvania_County_Boundaries' from data source 
  `/Users/jinhengcen/Documents/portfolio-setup-CenJinHeng/assignments/assignment_2/data/Pennsylvania_County_Boundaries.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 67 features and 19 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -8963377 ymin: 4825316 xmax: -8314404 ymax: 5201413
Projected CRS: WGS 84 / Pseudo-Mercator

hospitals <- st_read("data/hospitals.geojson")

Reading layer `hospitals' from data source 
  `/Users/jinhengcen/Documents/portfolio-setup-CenJinHeng/assignments/assignment_2/data/hospitals.geojson' 
  using driver `GeoJSON'
Simple feature collection with 223 features and 11 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -80.49621 ymin: 39.75163 xmax: -74.86704 ymax: 42.13403
Geodetic CRS:  WGS 84

census_tracts <- tracts(state = "PA", cb = TRUE)

hospitals <- st_transform(hospitals, st_crs(pa_counties))
census_tracts <- st_transform(census_tracts, st_crs(pa_counties))

# Check that all data loaded correctly

Questions to answer:

How many hospitals are in your dataset? 223
How many census tracts? 3445
What coordinate reference system is each dataset in? I converted them all in WGS 84 / Pseudo-Mercator

Step 2: Get Demographic Data

Use tidycensus to download tract-level demographic data for Pennsylvania.

Required variables:

Total population
Median household income
Population 65 years and over (you may need to sum multiple age categories)

Your Task:

# Get demographic data from ACS
tract_data <- get_acs(
  geography = "tract",
  state     = "42",
  variables = c(
    total_population = "B01003_001",
    median_household_income = "B19013_001"
  ),
  year = 2023,
  survey = "acs5",
  output = "wide",
  progress = FALSE
)

age_data <- get_acs(
  geography = "tract",
  state     = "42",
  variables = c(
    male_65 <- c("B01001_020","B01001_021","B01001_022","B01001_023","B01001_024","B01001_025"),
    female_65 <- c("B01001_044","B01001_045","B01001_046","B01001_047","B01001_048","B01001_049")
  ),
  year = 2023,
  survey = "acs5",
  progress = FALSE
) %>% 
  group_by (GEOID) %>%
  summarise(pop_65 = sum(estimate, na.rm = TRUE))


tract_data <- tract_data %>%
  left_join(age_data, by = "GEOID")


tract_data %>%
  summarise(missing_data = sum(is.na(median_household_incomeE)))

# A tibble: 1 × 1
  missing_data
         <int>
1           66

tract_data %>%
  summarise(median_income = sum(median(median_household_incomeE, na.rm = TRUE)))

# A tibble: 1 × 1
  median_income
          <dbl>
1        72944.

# Join to tract boundaries

census_tracts_joined <- census_tracts %>%
  left_join(tract_data, by = "GEOID")

Questions to answer:

What year of ACS data are you using? 2023
How many tracts have missing income data? 66
What is the median income across all PA census tracts? 72943.5

Step 3: Define Vulnerable Populations

Identify census tracts with vulnerable populations based on TWO criteria: 1. Low median household income (choose an appropriate threshold) 2. Significant elderly population (choose an appropriate threshold)

Your Task:

# Filter for vulnerable tracts based on your criteria

census_tracts_joined <- census_tracts_joined %>%
  mutate(
    percentage_65 = pop_65/total_populationE
  )

vulnerable_tracts <- census_tracts_joined %>%
  filter(median_household_incomeE < 48000,
         percentage_65 > 0.20
         )

Questions to answer:

What income threshold did you choose and why? For the income threshold, I chose 48,000 based on the 2025 poverty guidelines by the USCIS. For a family with four people, 150% of the federal poverty level is $48,225.
What elderly population threshold did you choose and why? For the age threshold, I chose the percentage of people over 65 years old is over 20%, which can be seen as an elderly community.
How many tracts meet your vulnerability criteria? 126
What percentage of PA census tracts are considered vulnerable by your definition? the percentage of vulnerable tracts = 126 / 3445 = 3.66%

Step 4: Calculate Distance to Hospitals

For each vulnerable tract, calculate the distance to the nearest hospital.

Your Task:

# Transform to appropriate projected CRS

vulnerable_tracts_proj <- vulnerable_tracts %>%
  st_transform(5070) # 5070 is for the whole country. For south PA alone, we can use 3365, which is more precise.

hospitals_proj <- hospitals %>%
  st_transform(5070)


# Calculate distance from each tract centroid to nearest hospital

tract_centroids <- st_centroid(vulnerable_tracts_proj)

nearest_hospital_idx <- st_nearest_feature(tract_centroids, hospitals_proj)

dist_to_hospital <- st_distance(
  tract_centroids,
  hospitals_proj[nearest_hospital_idx, ],
  by_element = TRUE
)


vulnerable_tracts_proj <- vulnerable_tracts_proj %>%
  mutate(
    dist_to_hospital = as.numeric(dist_to_hospital),
    dist_to_hospital_mi = dist_to_hospital * 0.000621371
  )

distant_data <- vulnerable_tracts_proj %>%
  summarise(
    avg_distance = mean(dist_to_hospital_mi, na.rm = TRUE),
    max_dist = max(dist_to_hospital_mi, na.rm = TRUE),
  )


num_over15 <- vulnerable_tracts_proj %>%
  filter(dist_to_hospital_mi > 15) %>%
  summarise(
    n_over15 = n(),
  )

Requirements:

Use an appropriate projected coordinate system for Pennsylvania
Calculate distances in miles
Explain why you chose your projection

Questions to answer:

What is the average distance to the nearest hospital for vulnerable tracts? 2.8 miles
What is the maximum distance? 18.6 miles
How many vulnerable tracts are more than 15 miles from the nearest hospital? 1

Step 5: Identify Underserved Areas

Define “underserved” as vulnerable tracts that are more than 15 miles from the nearest hospital.

Your Task:

# Create underserved variable

undeserved <- vulnerable_tracts_proj %>%
  filter(dist_to_hospital_mi > 15)

Questions to answer:

How many tracts are underserved? only 1!
What percentage of vulnerable tracts are underserved? 1/126 = 0.79%
Does this surprise you? Why or why not? No, I looked at the distance data of the vulnerable tracts, most of them (92.9%) are under 10 miles (16000m). I think 15 miles is a very big number, so we just have one tract fulfill all the requirements.

Step 6: Aggregate to County Level

Use spatial joins and aggregation to calculate county-level statistics about vulnerable populations and hospital access.

Your Task:

# Spatial join tracts to counties
vulnerable_tracts_proj <- st_transform(vulnerable_tracts_proj, st_crs(pa_counties))
undeserved  <- st_transform(undeserved , st_crs(pa_counties))

counties_num = census_tracts %>%
  st_centroid() %>%
  st_join(pa_counties%>% select(COUNTY_NAM)) %>%
  st_drop_geometry() %>%
  group_by(COUNTY_NAM) %>%
  summarize(
    n_tract =  n(),
  )%>%
  arrange(n_tract)

counties_vuln <- vulnerable_tracts_proj %>%
  st_centroid() %>%
  st_join(pa_counties%>% select(COUNTY_NAM)) %>%
  st_drop_geometry() %>%
  group_by(COUNTY_NAM) %>%
  summarize(
    n_vuln =  n(),
    avg_dist_vuln_mi = mean(dist_to_hospital_mi, na.rm = TRUE),
    total_vuln_pop = sum(pop_65, na.rm = TRUE)
  ) %>%
  arrange(desc(total_vuln_pop))

counties_unders <- undeserved %>%
  st_centroid() %>%
  st_join(pa_counties%>% select(COUNTY_NAM)) %>%
  st_drop_geometry() %>%
  group_by(COUNTY_NAM) %>%
  summarize(
    n_unders =  n(),
  ) %>%
  arrange(desc(n_unders))



# Aggregate statistics by county

counties_stat = counties_num %>%
  left_join(counties_vuln, by = "COUNTY_NAM") %>%
  left_join(counties_unders, by = "COUNTY_NAM") %>%
  mutate(
    percentage_unders = n_unders / n_vuln,
    percentage_vuln = n_vuln / n_tract
  )%>%
  arrange(desc(percentage_vuln))

Required county-level statistics:

Number of vulnerable tracts
Number of underserved tracts
Percentage of vulnerable tracts that are underserved
Average distance to nearest hospital for vulnerable tracts
Total vulnerable population

Questions to answer:

Which 5 counties have the highest percentage of underserved vulnerable tracts? CAMERON is the highest, with 100% in the number. Since I only have 1 underserved tract, I would show top 5 counties with the highest percentage of vulnerable tracts:

CAMERON 50.0%
FAYETTE 19.4%
CAMBRIA 19.0%
JEFFERSON 15.4%
WARREN 15.4%

Which counties have the most vulnerable people living far from hospitals? PHILADELPHIA, with the number of 18022
Are there any patterns in where underserved counties are located? Since I only have 1 underserved tract, I would observe patterns of vulnerable counties.

Most of them are from rural areas with less tracts number, so they have higher percentage of vulnerable tracts
They have large numbers of vulnerable population, which means lots of low-income and old people living there. Most of them have number over 1000.

Step 7: Create Summary Table

Create a professional table showing the top 10 priority counties for healthcare investment.

Your Task:

# Create and format priority counties table
# metrics for the priority: vulnerable index = normalized_number_of_vulnerable_tracts*0.3 + normalized_average_distant _hospital*0.5 + normalized_total_population*0.2

scale_minmax <- function(x) {
  rng <- range(x, na.rm = TRUE)
  if (!is.finite(diff(rng)) || diff(rng) == 0) return(rep(0.5, length(x)))
  (x - rng[1]) / diff(rng)
}

counties_stat_index <- counties_stat %>%
  mutate(
    n_vuln_norm = scale_minmax(n_vuln),
    avg_dist_vuln_mi_norm = scale_minmax(avg_dist_vuln_mi),
    total_vuln_pop_norm = scale_minmax(total_vuln_pop),
    percentage_vuln_norm = scale_minmax(percentage_vuln)
  ) %>%
   mutate(
     vuln_index =
       0.3  * n_vuln_norm +
       0.5 * avg_dist_vuln_mi_norm +
       0.2 * total_vuln_pop_norm
   ) %>%
  arrange(desc(vuln_index))


#make table

counties_table <- counties_stat_index %>%
  select(
    county_name = COUNTY_NAM,
    vulnerable_index = vuln_index,
    vulnerable_tract_number = n_vuln,
    average_dist_to_hospital = avg_dist_vuln_mi,
    total_vulnerable_pop = total_vuln_pop
  ) %>%
  mutate(
    vulnerable_index = round(vulnerable_index, 3),
    average_dist_to_hospital = round(average_dist_to_hospital, 2),
    total_vulnerable_pop = comma(total_vulnerable_pop)
  )%>%
  arrange(desc(vulnerable_index))

kable(
  counties_table,
  caption = "PA County-level Vulnerability Summary",
  align = "lcccc"
)

PA County-level Vulnerability Summary
county_name	vulnerable_index	vulnerable_tract_number	average_dist_to_hospital	total_vulnerable_pop
ALLEGHENY	0.519	28	2.39	15,213
CAMERON	0.502	1	18.56	504
PHILADELPHIA	0.409	19	0.89	18,022
HUNTINGDON	0.383	1	14.18	730
NORTHUMBERLAND	0.321	3	10.81	1,603
CAMBRIA	0.226	8	4.15	4,588
VENANGO	0.218	1	8.40	363
FAYETTE	0.183	7	2.55	5,780
ERIE	0.179	7	2.35	5,923
WARREN	0.178	2	6.19	1,257
LANCASTER	0.175	1	6.60	1,034
LUZERNE	0.153	6	2.51	4,195
WESTMORELAND	0.150	6	2.86	3,057
LAWRENCE	0.149	3	4.52	1,793
NORTHAMPTON	0.130	1	5.03	850
CLINTON	0.127	1	4.89	913
DELAWARE	0.120	6	1.32	4,179
LACKAWANNA	0.104	5	1.67	2,871
LEHIGH	0.092	1	3.42	1,417
BEAVER	0.076	2	2.53	1,291
BLAIR	0.076	2	2.18	2,079
INDIANA	0.070	1	2.63	1,447
WASHINGTON	0.068	2	2.39	915
LYCOMING	0.066	1	2.90	470
MERCER	0.063	3	1.42	1,866
JEFFERSON	0.057	2	1.72	1,566
MIFFLIN	0.038	1	1.87	516
YORK	0.028	1	1.55	425
CRAWFORD	0.015	1	0.93	787
WAYNE	0.014	1	0.83	951
SOMERSET	0.008	1	0.82	399
BUTLER	0.000	1	0.56	400
NA	NA	NA	NA	NA
FOREST	NA	NA	NA	NA
FULTON	NA	NA	NA	NA
MONTOUR	NA	NA	NA	NA
SULLIVAN	NA	NA	NA	NA
JUNIATA	NA	NA	NA	NA
POTTER	NA	NA	NA	NA
WYOMING	NA	NA	NA	NA
SNYDER	NA	NA	NA	NA
ELK	NA	NA	NA	NA
GREENE	NA	NA	NA	NA
PERRY	NA	NA	NA	NA
TIOGA	NA	NA	NA	NA
UNION	NA	NA	NA	NA
BEDFORD	NA	NA	NA	NA
MCKEAN	NA	NA	NA	NA
SUSQUEHANNA	NA	NA	NA	NA
CLARION	NA	NA	NA	NA
BRADFORD	NA	NA	NA	NA
COLUMBIA	NA	NA	NA	NA
CARBON	NA	NA	NA	NA
ARMSTRONG	NA	NA	NA	NA
CLEARFIELD	NA	NA	NA	NA
PIKE	NA	NA	NA	NA
ADAMS	NA	NA	NA	NA
FRANKLIN	NA	NA	NA	NA
LEBANON	NA	NA	NA	NA
CENTRE	NA	NA	NA	NA
SCHUYLKILL	NA	NA	NA	NA
MONROE	NA	NA	NA	NA
CUMBERLAND	NA	NA	NA	NA
DAUPHIN	NA	NA	NA	NA
BERKS	NA	NA	NA	NA
CHESTER	NA	NA	NA	NA
BUCKS	NA	NA	NA	NA
MONTGOMERY	NA	NA	NA	NA

Requirements:

Use knitr::kable() or similar for formatting
Include descriptive column names
Format numbers appropriately (commas for population, percentages, etc.)
Add an informative caption
Sort by priority (you decide the metric)

Part 2: Comprehensive Visualization

Using the skills from Week 3 (Data Visualization), create publication-quality maps and charts.

Map 1: County-Level Choropleth

Create a choropleth map showing healthcare access challenges at the county level.

Your Task:

# Create county-level access map
# I use percentage of vulnerable tract here

library(ggplot2)

counties_stat_sf <- pa_counties %>%
  select(COUNTY_NAM, geometry) %>%
  left_join(counties_stat, by = "COUNTY_NAM")

p1 <- ggplot() +
  # county base map
  geom_sf(
    data = counties_stat_sf,
    aes(fill = percentage_vuln),
    color = "white", size = 0.2
  ) +
  # hospitals
  geom_sf(
    data = hospitals_proj,
    shape = 21, fill = "red",
    size = 1.8, alpha = 0.9
  ) +
  # legend
  scale_fill_viridis_c(
    name = "% vulnerable tracts",
    labels = label_percent(accuracy = 1),
    limits = c(0, 1),
    option = "C",      
    direction = -1
  ) +
  coord_sf() +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    legend.title = element_text(size = 10, face = "bold"),
    legend.text  = element_text(size = 9),
    plot.title   = element_text(face = "bold", size = 16),
    plot.subtitle= element_text(size = 12)
  ) +
  labs(
    title    = "Healthcare Access Challenges by County",
    subtitle = "share of vulnerable census tracts",
    caption  = "Source: analysis based on income and percentage of people over 65 years old."
  ) +
  guides(fill = guide_colorbar(
    barheight = unit(70, "pt"),
    barwidth  = unit(8,  "pt"),
    ticks.colour = "black"
  ))

p1

Requirements:

Fill counties by percentage of vulnerable tracts that are underserved
Include hospital locations as points
Use an appropriate color scheme
Include clear title, subtitle, and caption
Use theme_void() or similar clean theme
Add a legend with formatted labels

Map 2: Detailed Vulnerability Map

Create a map highlighting underserved vulnerable tracts.

Your Task:

# Create detailed tract-level map

p2 <- ggplot() +
  # tract base map
  geom_sf(
    data = census_tracts,
    color = "grey", size = 0.1
  ) +
  # county base map
  geom_sf(
    data = pa_counties,
    color = "lightblue", size = 0.5,fill = NA,
  ) +
  # hospitals
  geom_sf(
    data = hospitals_proj,
    shape = 16, color = "red",
    size = 1, alpha = 0.9
  ) +
  # underserved community
  geom_sf(
    data = undeserved,
    color = "blue"
  )+
  coord_sf() +
  theme_void(base_size = 12) +
  theme(
    plot.title   = element_text(face = "bold", size = 16),
    plot.subtitle= element_text(size = 12)
  ) +
  labs(
    title    = "Underserved Communities",
    subtitle = "Blue tracts represent underserved communities; red points show hospital locations."
  ) +
  guides(fill = guide_colorbar(
    barheight = unit(70, "pt"),
    barwidth  = unit(8,  "pt"),
    ticks.colour = "black"
  ))

p2

#only one very small blue tract in the center of the map

Requirements:

Show underserved vulnerable tracts in a contrasting color
Include county boundaries for context
Show hospital locations
Use appropriate visual hierarchy (what should stand out?)
Include informative title and subtitle

Chart: Distribution Analysis

Create a visualization showing the distribution of distances to hospitals for vulnerable populations.

Your Task:

# Create distribution visualization

# Histogram or density plot of distances
ggplot(vulnerable_tracts_proj) +
  aes(x = dist_to_hospital_mi) +
  geom_histogram(bins = 15, fill = "steelblue", alpha = 0.7) +
  labs(
    title = "Distribution of distance to the nearest hospital",
    x = "distance to the nearest hospital",
    y = "Number of vulnerable tracts",
    caption  = "Most of tracts have distance between 0 and 5."
  ) +
  theme_minimal()

# Box plot comparing distances across regions
ggplot(vulnerable_tracts_proj) +
  aes(x = NAMELSADCO, y = dist_to_hospital_mi, fill = NAMELSADCO) +
  geom_boxplot() +
  labs(
    title = "Distances by County Category",
    x = "County",
    y = "Distance",
    caption  = "While most counties show little variation, four counties have huge internal differences"
  ) +
  theme_minimal() +
  theme(legend.position = "none")+
  theme(
  axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7)
)

# Bar chart of underserved tracts by county
#only 1 underserved tracts in my study, it is Census Tract 9601 in the Cameron County


# Scatter plot of distance vs. vulnerable population size
ggplot(vulnerable_tracts_proj) +
  aes(x = dist_to_hospital_mi, y = total_populationE) +
  geom_point(color = "steelblue", alpha = 0.6, size = 2) +
  geom_smooth(
    method = "lm",              
    se = TRUE,                  
    color = "darkred",          
    fill = "pink",              
    linewidth = 1
  ) +
  labs(
    title = "distance vs. vulnerable population size",
    x = "Distance to the nearest hospital",
    y = "Vulnerable population size",
    caption  = str_wrap("More vulnerable population, little distance to hospital. Since hospitals are designed to located near population clusters, and we didn't limit this effect in this study ", width = 90)
  ) +
  theme_minimal() +
  scale_x_continuous() +
  scale_y_continuous(labels = comma)

Suggested chart types:

Histogram or density plot of distances
Box plot comparing distances across regions
Bar chart of underserved tracts by county
Scatter plot of distance vs. vulnerable population size

Requirements:

Clear axes labels with units
Appropriate title
Professional formatting
Brief interpretation (1-2 sentences as a caption or in text)

Part 3: Bring Your Own Data Analysis

Choose your own additional spatial dataset and conduct a supplementary analysis.

Challenge Options

Choose ONE of the following challenge exercises, or propose your own research question using OpenDataPhilly data (https://opendataphilly.org/datasets/).

Note these are just loose suggestions to spark ideas - follow or make your own as the data permits and as your ideas evolve. This analysis should include bringing in your own dataset, ensuring the projection/CRS of your layers align and are appropriate for the analysis (not lat/long or geodetic coordinate systems). The analysis portion should include some combination of spatial and attribute operations to answer a relatively straightforward question

Education & Youth Services

Option A: Educational Desert Analysis

Data: Schools, Libraries, Recreation Centers, Census tracts (child population)
Question: “Which neighborhoods lack adequate educational infrastructure for children?”
Operations: Buffer schools/libraries (0.5 mile walking distance), identify coverage gaps, overlay with child population density
Policy relevance: School district planning, library placement, after-school program siting

Option B: School Safety Zones - Data: Schools, Crime Incidents, Bike Network - Question: “Are school zones safe for walking/biking, or are they crime hotspots?” - Operations: Buffer schools (1000ft safety zone), spatial join with crime incidents, assess bike infrastructure coverage - Policy relevance: Safe Routes to School programs, crossing guard placement

Environmental Justice

Option C: Green Space Equity

Data: Parks, Street Trees, Census tracts (race/income demographics)
Question: “Do low-income and minority neighborhoods have equitable access to green space?”
Operations: Buffer parks (10-minute walk = 0.5 mile), calculate tree canopy or park acreage per capita, compare by demographics
Policy relevance: Climate resilience, environmental justice, urban forestry investment

Public Safety & Justice

Option D: Crime & Community Resources

Data: Crime Incidents, Recreation Centers, Libraries, Street Lights
Question: “Are high-crime areas underserved by community resources?”
Operations: Aggregate crime counts to census tracts or neighborhoods, count community resources per area, spatial correlation analysis
Policy relevance: Community investment, violence prevention strategies

Infrastructure & Services

Option E: Polling Place Accessibility

Data: Polling Places, SEPTA stops, Census tracts (elderly population, disability rates)
Question: “Are polling places accessible for elderly and disabled voters?”
Operations: Buffer polling places and transit stops, identify vulnerable populations, find areas lacking access
Policy relevance: Voting rights, election infrastructure, ADA compliance

Health & Wellness

Option F: Recreation & Population Health

Data: Recreation Centers, Playgrounds, Parks, Census tracts (demographics)
Question: “Is lack of recreation access associated with vulnerable populations?”
Operations: Calculate recreation facilities per capita by neighborhood, buffer facilities for walking access, overlay with demographic indicators
Policy relevance: Public health investment, recreation programming, obesity prevention

Emergency Services

Option G: EMS Response Coverage

Data: Fire Stations, EMS stations, Population density, High-rise buildings
Question: “Are population-dense areas adequately covered by emergency services?”
Operations: Create service area buffers (5-minute drive = ~2 miles), assess population coverage, identify gaps in high-density areas
Policy relevance: Emergency preparedness, station siting decisions

Arts & Culture

Option H: Cultural Asset Distribution

Data: Public Art, Museums, Historic sites/markers, Neighborhoods
Question: “Do all neighborhoods have equitable access to cultural amenities?”
Operations: Count cultural assets per neighborhood, normalize by population, compare distribution across demographic groups
Policy relevance: Cultural equity, tourism, quality of life, neighborhood identity

Data Sources

OpenDataPhilly: https://opendataphilly.org/datasets/

Most datasets available as GeoJSON, Shapefile, or CSV with coordinates
Always check the Metadata for a data dictionary of the fields.

Additional Sources:

Pennsylvania Open Data: https://data.pa.gov/
Census Bureau (via tidycensus): Demographics, economic indicators, commute patterns
TIGER/Line (via tigris): Geographic boundaries

Recommended Starting Points

If you’re feeling confident: Choose an advanced challenge with multiple data layers. If you are a beginner, choose something more manageable that helps you understand the basics

If you have a different idea: Propose your own question! Just make sure:

You can access the spatial data
You can perform at least 2 spatial operations

Your Analysis

Your Task:

Find and load additional data
- Document your data source
- Check and standardize the CRS
- Provide basic summary statistics

# Load your additional dataset

library(sf)

schools <- st_read("Data/Schools/Schools.shp")

Reading layer `Schools' from data source 
  `/Users/jinhengcen/Documents/portfolio-setup-CenJinHeng/assignments/assignment_2/data/Schools/Schools.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 495 features and 14 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -8378628 ymin: 4852555 xmax: -8345686 ymax: 4884813
Projected CRS: WGS 84 / Pseudo-Mercator

crime <- st_read("Data/Crime/Crime.shp")

Reading layer `Crime' from data source 
  `/Users/jinhengcen/Documents/portfolio-setup-CenJinHeng/assignments/assignment_2/data/Crime/Crime.shp' 
  using driver `ESRI Shapefile'
replacing null geometries with empty geometries
Simple feature collection with 160388 features and 13 fields (with 6744 geometries empty)
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -80.50237 ymin: 39.87688 xmax: -74.95784 ymax: 42.22434
Geodetic CRS:  WGS 84

bike_network <- st_read("Data/Bike_Network/Bike_Network.shp")

Reading layer `Bike_Network' from data source 
  `/Users/jinhengcen/Documents/portfolio-setup-CenJinHeng/assignments/assignment_2/data/Bike_Network/Bike_Network.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 5225 features and 8 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: -8378937 ymin: 4847835 xmax: -8345146 ymax: 4883978
Projected CRS: WGS 84 / Pseudo-Mercator

schools <- st_transform(schools, 2272)
crime <- st_transform(crime, 2272)
bike_network <- st_transform(bike_network, 2272)

library(dplyr)
library(ggplot2)

## school type
schools %>%
  st_drop_geometry() %>%
  count(type_speci, sort = TRUE) %>%
  ggplot(aes(x = reorder(type_speci, n), y = n)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  coord_flip() +
  labs(
    title = "Distribution of school types",
    x = "School type",
    y = "Number"
  ) +
  theme_minimal(base_size = 13)

# Crime Hour
crime %>%
  st_drop_geometry() %>%
  count(hour) %>%
  ggplot(aes(x = hour, y = n)) +
  geom_col(fill = "firebrick") +
  labs(
    title = "Distribution of crime hour",
    x = "Hour",
    y = "Number of incidents"
  ) +
  theme_minimal(base_size = 13)

#crime types
crime %>%
  st_drop_geometry() %>%
  count(text_gener, sort = TRUE) %>%
  slice_max(n, n = 10) %>%  
  ggplot(aes(x = reorder(text_gener, n), y = n)) +
  geom_bar(stat = "identity", fill = "orange") +
  coord_flip() +
  labs(
    title = "Top 10 Crime Types",
    x = "Types",
    y = "Number of incidents"
  ) +
  theme_minimal(base_size = 13)

# Bike lane
bike_length_summary <- bike_network %>%
  st_drop_geometry() %>%
  group_by(TYPE) %>%
  summarise(total_length = sum(Shape__Len, na.rm = TRUE)) %>%
  arrange(desc(total_length))

ggplot(bike_length_summary, aes(x = reorder(TYPE, total_length), y = total_length)) +
  geom_bar(stat = "identity", fill = "forestgreen") +
  coord_flip() +
  labs(
    title = "Distribution of Bike Lane Type",
    x = "Type",
    y = "Total Length"
  ) +
  theme_minimal(base_size = 13)

Questions to answer:

What dataset did you choose and why? Three data sets are chosen:
- Schools data in shp format
- Crime incidents data points in shp format
- Bike_Network in shp format Since I am going to calculate some metrics like the numbers of crime incidents and the length of bike lanes within school buffers, these data are needed.
What is the data source and date? They are all from OpenDataPhilly. All of them are up_to_date. The crime incidents data is from 2024, because we are in the October of the 2025, so we don’t have data for the whole 2025. Using 2024 can avoid some time variation.
How many features does it contain? All of them have geometry, which means we can run them in spatial analysis. For the school data set, we got names, types, grade levels, phone numbers, street names, and zip codes. For the crime data, we have precise time, crime types, location details. For the bike network data, we knew street names, types, lengths and oneway.
What CRS is it in? Did you need to transform it? EPSG:2272, which is used in south Pennsylvania.

Pose a research question

Write a clear research statement that your analysis will answer.

Examples:

“Do vulnerable tracts have adequate public transit access to hospitals?”
“Are EMS stations appropriately located near vulnerable populations?”
“Do areas with low vehicle access have worse hospital access?”

My question is “Are school zones safe for walking/biking, or are they crime hotspots?”

Conduct spatial analysis

Use at least TWO spatial operations to answer your research question.

Required operations (choose 2+):

Buffers
Spatial joins
Spatial filtering with predicates
Distance calculations
Intersections or unions
Point-in-polygon aggregation

Your Task:

# Your spatial analysis

# I have loaded data sets and make simple statistical analysis for each of them in the previous chunk

# Create buffer for schools

schools <- schools %>% mutate(school_id = row_number())
zones <- st_buffer(schools, dist = 1000)

# Calculate the crime number

crime_by_zone <- crime %>%
  st_join(zones %>% select(school_id), join = st_within) %>%
  st_drop_geometry() %>%
  count(school_id, name = "crime_count")


# Calculate the bike lane length
bike_in_zone <- st_intersection(bike_network, zones %>% select(school_id))

bike_len_by_zone <- bike_in_zone %>%
  mutate(len_ft = as.numeric(st_length(.))) %>% 
  st_drop_geometry() %>%
  group_by(school_id) %>%
  summarise(bike_len_ft = sum(len_ft, na.rm = TRUE), .groups = "drop")

# Combine the result
zone_stats <- zones %>%
  left_join(crime_by_zone,    by = "school_id") %>%
  left_join(bike_len_by_zone, by = "school_id")

# Calculate average number of the crime incidents and bike lane length
zone_summary <- zone_stats %>%
  st_drop_geometry() %>%
  summarise(
    mean_crime = mean(crime_count, na.rm = TRUE),
    mean_bike_len = mean(bike_len_ft, na.rm = TRUE)
  )

city_summary <- tibble(
  mean_crime = nrow(crime) / nrow(zones),
  mean_bike_len = sum(st_length(bike_network)) / nrow(zones)
)

# Combine results
comparison <- rbind(
  data.frame(Category = "School Zones", 
             mean_crime = zone_summary$mean_crime, 
             mean_bike_len = zone_summary$mean_bike_len),
  data.frame(Category = "Citywide Average",
             mean_crime = city_summary$mean_crime,
             mean_bike_len = city_summary$mean_bike_len)
)

# Draw diagram
ggplot(comparison, aes(x = Category, y = mean_crime, fill = Category)) +
  geom_bar(stat = "identity", width = 0.6) +
  labs(
    title = "Comperison of Average Crime Incidents: School Zones VS City",
    y = "Averge Crime Incidents"
  ) +
  theme_minimal(base_size = 14) +
  theme(legend.position = "none")

ggplot(comparison, aes(x = Category, y = mean_bike_len, fill = Category)) +
  geom_bar(stat = "identity", width = 0.6) +
  labs(
    title = "Comperison of Bike Lane Length: School Zones VS City",
    y = "Average Bike Lane Length"
  ) +
  theme_minimal(base_size = 14) +
  theme(legend.position = "none")

# Mapping

# Get tracts of Philly
tracts_phl <- get_acs(
  geography = "tract",
  state     = "PA",
  county    = "Philadelphia",
  variables = "B01003_001",
  survey    = "acs5",
  year      = 2022,
  geometry  = TRUE,
  cache_table = TRUE
) %>%
  select(GEOID, NAME, pop = estimate, geometry) %>%
  st_transform(2272)    

# point_on_surface
zone_points <- zone_stats |>
  mutate(geometry = st_point_on_surface(geometry),
         bike_len_mile = bike_len_ft / 5280) |>
  st_as_sf()

# Crime point map
ggplot() +
  geom_sf(data = tracts_phl, fill = NA, color = "grey", linewidth = 0.2) +
  geom_sf(data = zone_points, aes(size = crime_count), color = "firebrick", alpha = 0.6) +
  scale_size_continuous(name = "Crime count", range = c(0, 4)) +
  labs(title = "School Zones: Crime Counts (Point size = count)", x = NULL, y = NULL) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "right")

# Bike lane point map
ggplot() +
  geom_sf(data = tracts_phl, fill = NA, color = "grey", linewidth = 0.2) +
  geom_sf(data = zone_points, aes(size = bike_len_mile), color = "forestgreen", alpha = 0.6) +
  scale_size_continuous(name = "Bike lane (miles)", range = c(0.1, 2)) +
  labs(title = "School Zones: Bike Lane Length (Point size = miles)", x = NULL, y = NULL) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "right")

Analysis requirements:

Clear code comments explaining each step
Appropriate CRS transformations
Summary statistics or counts
At least one map showing your findings
Brief interpretation of results (3-5 sentences)

Your interpretation:

School zones have fewer crime incidents and bike lane length compared to city average. More pedestrian-friendly projects should be applied to school areas. For the spatial difference, higher concentrations of crimes are observed around schools in central and southern Philadelphia. School zones with longer bike lane coverage are mostly located in the city’s core and western areas. Planning efforts should focus on improving safety in school zones with higher crime risks, especially in central and southern Philadelphia. Expanding connected and protected bike lanes around schools can promote safer and more accessible environments for students.

Finally - A few comments about your incorporation of feedback!

Take a few moments to clean up your markdown document and then write a line or two or three about how you may have incorporated feedback that you recieved after your first assignment.

I need to hide my API Key and printed tables!

Submission Requirements

What to submit:

Rendered HTML document posted to your course portfolio with all code, outputs, maps, and text
- Use embed-resources: true in YAML so it’s a single file
- All code should run without errors
- All maps and charts should display correctly

File naming: LastName_FirstName_Assignment2.html and LastName_FirstName_Assignment2.qmd

Assignment 2: Spatial Analysis and Visualization

Assignment Overview

Part 1: Healthcare Access for Vulnerable Populations

Research Question

Required Analysis Steps

Step 1: Data Collection (5 points)

Step 2: Get Demographic Data

Step 3: Define Vulnerable Populations

Step 4: Calculate Distance to Hospitals

Step 5: Identify Underserved Areas

Step 6: Aggregate to County Level

Step 7: Create Summary Table

Part 2: Comprehensive Visualization

Map 1: County-Level Choropleth

Map 2: Detailed Vulnerability Map

Chart: Distribution Analysis

Part 3: Bring Your Own Data Analysis

Challenge Options

Education & Youth Services

Environmental Justice

Policy relevance: Climate resilience, environmental justice, urban forestry investment

Public Safety & Justice

Policy relevance: Community investment, violence prevention strategies

Infrastructure & Services

Health & Wellness

Emergency Services

Arts & Culture

Data Sources

Recommended Starting Points

Your Analysis

Finally - A few comments about your incorporation of feedback!

Submission Requirements